论文标题
通过主动探测和角膜反射的视频会议中的实时深击检测
Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection
论文作者
论文摘要
近年来，COVID大流行导致了在线视频通话的广泛采用。但是，越来越多的对视频通话的依赖为使用先进的实时深击欺诈者提供了新的模仿攻击的机会。实时的深击对检测方法提出了新的挑战，这些方法必须实时运行，因为视频通话正在进行中。在本文中，我们描述了一种新的主动法医方法来检测实时深击。具体来说，我们通过在屏幕上显示独特的图案并使用从呼叫参与者脸的图像中提取的角膜反射来验证视频呼叫。可以通过在共享屏幕上显示的呼叫参与者或直接集成到视频通话客户端来引起这种模式。无论哪种情况，都不需要专业的成像或照明硬件。通过大规模的模拟，我们在各种现实的成像方案中评估了这种方法的可靠性。
The COVID pandemic has led to the wide adoption of online video calls in recent years. However, the increasing reliance on video calls provides opportunities for new impersonation attacks by fraudsters using the advanced real-time DeepFakes. Real-time DeepFakes pose new challenges to detection methods, which have to run in real-time as a video call is ongoing. In this paper, we describe a new active forensic method to detect real-time DeepFakes. Specifically, we authenticate video calls by displaying a distinct pattern on the screen and using the corneal reflection extracted from the images of the call participant's face. This pattern can be induced by a call participant displaying on a shared screen or directly integrated into the video-call client. In either case, no specialized imaging or lighting hardware is required. Through large-scale simulations, we evaluate the reliability of this approach under a range in a variety of real-world imaging scenarios.
